Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations4424
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory344.7 B

Variable types

Numeric28
Categorical9

Alerts

Admission_grade is highly overall correlated with Previous_qualification_gradeHigh correlation
Age_at_enrollment is highly overall correlated with Application_modeHigh correlation
Application_mode is highly overall correlated with Age_at_enrollmentHigh correlation
Course is highly overall correlated with Daytime_evening_attendanceHigh correlation
Curricular_units_1st_sem_approved is highly overall correlated with Curricular_units_1st_sem_enrolled and 4 other fieldsHigh correlation
Curricular_units_1st_sem_credited is highly overall correlated with Curricular_units_2nd_sem_creditedHigh correlation
Curricular_units_1st_sem_enrolled is highly overall correlated with Curricular_units_1st_sem_approved and 2 other fieldsHigh correlation
Curricular_units_1st_sem_evaluations is highly overall correlated with Curricular_units_2nd_sem_evaluationsHigh correlation
Curricular_units_1st_sem_grade is highly overall correlated with Curricular_units_1st_sem_approved and 2 other fieldsHigh correlation
Curricular_units_2nd_sem_approved is highly overall correlated with Curricular_units_1st_sem_approved and 5 other fieldsHigh correlation
Curricular_units_2nd_sem_credited is highly overall correlated with Curricular_units_1st_sem_creditedHigh correlation
Curricular_units_2nd_sem_enrolled is highly overall correlated with Curricular_units_1st_sem_approved and 2 other fieldsHigh correlation
Curricular_units_2nd_sem_evaluations is highly overall correlated with Curricular_units_1st_sem_evaluationsHigh correlation
Curricular_units_2nd_sem_grade is highly overall correlated with Curricular_units_1st_sem_approved and 2 other fieldsHigh correlation
Daytime_evening_attendance is highly overall correlated with CourseHigh correlation
International is highly overall correlated with NacionalityHigh correlation
Nacionality is highly overall correlated with InternationalHigh correlation
Previous_qualification_grade is highly overall correlated with Admission_gradeHigh correlation
Status is highly overall correlated with Curricular_units_2nd_sem_approvedHigh correlation
Daytime_evening_attendance is highly imbalanced (50.3%) Imbalance
Educational_special_needs is highly imbalanced (90.9%) Imbalance
International is highly imbalanced (83.2%) Imbalance
Mothers_occupation has 144 (3.3%) zeros Zeros
Fathers_occupation has 128 (2.9%) zeros Zeros
Curricular_units_1st_sem_credited has 3847 (87.0%) zeros Zeros
Curricular_units_1st_sem_enrolled has 180 (4.1%) zeros Zeros
Curricular_units_1st_sem_evaluations has 349 (7.9%) zeros Zeros
Curricular_units_1st_sem_approved has 718 (16.2%) zeros Zeros
Curricular_units_1st_sem_grade has 718 (16.2%) zeros Zeros
Curricular_units_1st_sem_without_evaluations has 4130 (93.4%) zeros Zeros
Curricular_units_2nd_sem_credited has 3894 (88.0%) zeros Zeros
Curricular_units_2nd_sem_enrolled has 180 (4.1%) zeros Zeros
Curricular_units_2nd_sem_evaluations has 401 (9.1%) zeros Zeros
Curricular_units_2nd_sem_approved has 870 (19.7%) zeros Zeros
Curricular_units_2nd_sem_grade has 870 (19.7%) zeros Zeros
Curricular_units_2nd_sem_without_evaluations has 4142 (93.6%) zeros Zeros

Reproduction

Analysis started2025-01-05 14:48:44.342185
Analysis finished2025-01-05 14:52:02.946447
Duration3 minutes and 18.6 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Marital_status
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1785714
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:03.186764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.60574695
Coefficient of variation (CV)0.51396711
Kurtosis21.482639
Mean1.1785714
Median Absolute Deviation (MAD)0
Skewness4.3997643
Sum5214
Variance0.36692936
MonotonicityNot monotonic
2025-01-05T21:52:03.497384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 3919
88.6%
2 379
 
8.6%
4 91
 
2.1%
5 25
 
0.6%
6 6
 
0.1%
3 4
 
0.1%
ValueCountFrequency (%)
1 3919
88.6%
2 379
 
8.6%
3 4
 
0.1%
4 91
 
2.1%
5 25
 
0.6%
6 6
 
0.1%
ValueCountFrequency (%)
6 6
 
0.1%
5 25
 
0.6%
4 91
 
2.1%
3 4
 
0.1%
2 379
 
8.6%
1 3919
88.6%

Application_mode
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.669078
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:03.673577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median17
Q339
95-th percentile44
Maximum57
Range56
Interquartile range (IQR)38

Descriptive statistics

Standard deviation17.484682
Coefficient of variation (CV)0.93655844
Kurtosis-1.4538061
Mean18.669078
Median Absolute Deviation (MAD)16
Skewness0.39303572
Sum82592
Variance305.71411
MonotonicityNot monotonic
2025-01-05T21:52:03.847484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 1708
38.6%
17 872
19.7%
39 785
17.7%
43 312
 
7.1%
44 213
 
4.8%
7 139
 
3.1%
18 124
 
2.8%
42 77
 
1.7%
51 59
 
1.3%
16 38
 
0.9%
Other values (8) 97
 
2.2%
ValueCountFrequency (%)
1 1708
38.6%
2 3
 
0.1%
5 16
 
0.4%
7 139
 
3.1%
10 10
 
0.2%
15 30
 
0.7%
16 38
 
0.9%
17 872
19.7%
18 124
 
2.8%
26 1
 
< 0.1%
ValueCountFrequency (%)
57 1
 
< 0.1%
53 35
 
0.8%
51 59
 
1.3%
44 213
 
4.8%
43 312
 
7.1%
42 77
 
1.7%
39 785
17.7%
27 1
 
< 0.1%
26 1
 
< 0.1%
18 124
 
2.8%

Application_order
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7278481
Minimum0
Maximum9
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:04.040045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3137931
Coefficient of variation (CV)0.76036376
Kurtosis2.6512887
Mean1.7278481
Median Absolute Deviation (MAD)0
Skewness1.88105
Sum7644
Variance1.7260523
MonotonicityNot monotonic
2025-01-05T21:52:04.277543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3026
68.4%
2 547
 
12.4%
3 309
 
7.0%
4 249
 
5.6%
5 154
 
3.5%
6 137
 
3.1%
9 1
 
< 0.1%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 3026
68.4%
2 547
 
12.4%
3 309
 
7.0%
4 249
 
5.6%
5 154
 
3.5%
6 137
 
3.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
6 137
 
3.1%
5 154
 
3.5%
4 249
 
5.6%
3 309
 
7.0%
2 547
 
12.4%
1 3026
68.4%
0 1
 
< 0.1%

Course
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8856.6426
Minimum33
Maximum9991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:04.497607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile171
Q19085
median9238
Q39556
95-th percentile9991
Maximum9991
Range9958
Interquartile range (IQR)471

Descriptive statistics

Standard deviation2063.5664
Coefficient of variation (CV)0.23299646
Kurtosis13.199149
Mean8856.6426
Median Absolute Deviation (MAD)262
Skewness-3.8091352
Sum39181787
Variance4258306.4
MonotonicityNot monotonic
2025-01-05T21:52:04.708065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
9500 766
17.3%
9147 380
 
8.6%
9238 355
 
8.0%
9085 337
 
7.6%
9773 331
 
7.5%
9670 268
 
6.1%
9991 268
 
6.1%
9254 252
 
5.7%
9070 226
 
5.1%
171 215
 
4.9%
Other values (7) 1026
23.2%
ValueCountFrequency (%)
33 12
 
0.3%
171 215
4.9%
8014 215
4.9%
9003 210
4.7%
9070 226
5.1%
9085 337
7.6%
9119 170
3.8%
9130 141
 
3.2%
9147 380
8.6%
9238 355
8.0%
ValueCountFrequency (%)
9991 268
 
6.1%
9853 192
 
4.3%
9773 331
7.5%
9670 268
 
6.1%
9556 86
 
1.9%
9500 766
17.3%
9254 252
 
5.7%
9238 355
8.0%
9147 380
8.6%
9130 141
 
3.2%

Daytime_evening_attendance
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.1 KiB
1
3941 
0
483 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Length

2025-01-05T21:52:04.926924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T21:52:05.157799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Most occurring characters

ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3941
89.1%
0 483
 
10.9%

Previous_qualification
Real number (ℝ)

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5777577
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:05.371074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile39
Maximum43
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.216592
Coefficient of variation (CV)2.2317897
Kurtosis6.7781662
Mean4.5777577
Median Absolute Deviation (MAD)0
Skewness2.8712068
Sum20252
Variance104.37876
MonotonicityNot monotonic
2025-01-05T21:52:05.603307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3717
84.0%
39 219
 
5.0%
19 162
 
3.7%
3 126
 
2.8%
12 45
 
1.0%
40 40
 
0.9%
42 36
 
0.8%
2 23
 
0.5%
6 16
 
0.4%
9 11
 
0.2%
Other values (7) 29
 
0.7%
ValueCountFrequency (%)
1 3717
84.0%
2 23
 
0.5%
3 126
 
2.8%
4 8
 
0.2%
5 1
 
< 0.1%
6 16
 
0.4%
9 11
 
0.2%
10 4
 
0.1%
12 45
 
1.0%
14 1
 
< 0.1%
ValueCountFrequency (%)
43 6
 
0.1%
42 36
 
0.8%
40 40
 
0.9%
39 219
5.0%
38 7
 
0.2%
19 162
3.7%
15 2
 
< 0.1%
14 1
 
< 0.1%
12 45
 
1.0%
10 4
 
0.1%

Previous_qualification_grade
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.61331
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:05.843763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile110
Q1125
median133.1
Q3140
95-th percentile157
Maximum190
Range95
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.188332
Coefficient of variation (CV)0.09944953
Kurtosis0.96825772
Mean132.61331
Median Absolute Deviation (MAD)7.1
Skewness0.31286749
Sum586681.3
Variance173.93209
MonotonicityNot monotonic
2025-01-05T21:52:06.103021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133.1 491
 
11.1%
130 375
 
8.5%
140 336
 
7.6%
120 278
 
6.3%
150 162
 
3.7%
125 122
 
2.8%
135 108
 
2.4%
110 101
 
2.3%
131 99
 
2.2%
160 95
 
2.1%
Other values (91) 2257
51.0%
ValueCountFrequency (%)
95 1
 
< 0.1%
96 2
 
< 0.1%
97 1
 
< 0.1%
99 2
 
< 0.1%
100 76
1.7%
101 6
 
0.1%
102 5
 
0.1%
103 3
 
0.1%
105 4
 
0.1%
106 10
 
0.2%
ValueCountFrequency (%)
190 2
 
< 0.1%
188 1
 
< 0.1%
184.4 1
 
< 0.1%
182 1
 
< 0.1%
180 9
0.2%
178 2
 
< 0.1%
177 2
 
< 0.1%
176 1
 
< 0.1%
175 1
 
< 0.1%
174 1
 
< 0.1%

Nacionality
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8731917
Minimum1
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:06.359422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum109
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.914514
Coefficient of variation (CV)3.6913008
Kurtosis135.14621
Mean1.8731917
Median Absolute Deviation (MAD)0
Skewness10.703998
Sum8287
Variance47.810504
MonotonicityNot monotonic
2025-01-05T21:52:06.631775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 4314
97.5%
41 38
 
0.9%
26 14
 
0.3%
22 13
 
0.3%
6 13
 
0.3%
24 5
 
0.1%
100 3
 
0.1%
11 3
 
0.1%
103 3
 
0.1%
21 2
 
< 0.1%
Other values (11) 16
 
0.4%
ValueCountFrequency (%)
1 4314
97.5%
2 2
 
< 0.1%
6 13
 
0.3%
11 3
 
0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
17 1
 
< 0.1%
21 2
 
< 0.1%
22 13
 
0.3%
24 5
 
0.1%
ValueCountFrequency (%)
109 1
 
< 0.1%
108 1
 
< 0.1%
105 2
 
< 0.1%
103 3
 
0.1%
101 2
 
< 0.1%
100 3
 
0.1%
62 2
 
< 0.1%
41 38
0.9%
32 1
 
< 0.1%
26 14
 
0.3%

Mothers_qualification
Real number (ℝ)

Distinct29
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.561935
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:06.857965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median19
Q337
95-th percentile38
Maximum44
Range43
Interquartile range (IQR)35

Descriptive statistics

Standard deviation15.603186
Coefficient of variation (CV)0.79763001
Kurtosis-1.6922924
Mean19.561935
Median Absolute Deviation (MAD)18
Skewness0.0019784779
Sum86542
Variance243.45942
MonotonicityNot monotonic
2025-01-05T21:52:07.093392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 1069
24.2%
37 1009
22.8%
19 953
21.5%
38 562
12.7%
3 438
9.9%
34 130
 
2.9%
2 83
 
1.9%
4 49
 
1.1%
12 42
 
0.9%
5 21
 
0.5%
Other values (19) 68
 
1.5%
ValueCountFrequency (%)
1 1069
24.2%
2 83
 
1.9%
3 438
9.9%
4 49
 
1.1%
5 21
 
0.5%
6 4
 
0.1%
9 8
 
0.2%
10 3
 
0.1%
11 3
 
0.1%
12 42
 
0.9%
ValueCountFrequency (%)
44 1
 
< 0.1%
43 4
 
0.1%
42 4
 
0.1%
41 6
 
0.1%
40 9
 
0.2%
39 8
 
0.2%
38 562
12.7%
37 1009
22.8%
36 3
 
0.1%
35 3
 
0.1%

Fathers_qualification
Real number (ℝ)

Distinct34
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.275316
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:07.317935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median19
Q337
95-th percentile38
Maximum44
Range43
Interquartile range (IQR)34

Descriptive statistics

Standard deviation15.343108
Coefficient of variation (CV)0.68879416
Kurtosis-1.5805918
Mean22.275316
Median Absolute Deviation (MAD)18
Skewness-0.29869722
Sum98546
Variance235.41096
MonotonicityNot monotonic
2025-01-05T21:52:07.566367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
37 1209
27.3%
19 968
21.9%
1 904
20.4%
38 702
15.9%
3 282
 
6.4%
34 112
 
2.5%
2 68
 
1.5%
4 39
 
0.9%
12 38
 
0.9%
39 20
 
0.5%
Other values (24) 82
 
1.9%
ValueCountFrequency (%)
1 904
20.4%
2 68
 
1.5%
3 282
 
6.4%
4 39
 
0.9%
5 18
 
0.4%
6 2
 
< 0.1%
9 5
 
0.1%
10 2
 
< 0.1%
11 10
 
0.2%
12 38
 
0.9%
ValueCountFrequency (%)
44 1
 
< 0.1%
43 2
 
< 0.1%
42 1
 
< 0.1%
41 2
 
< 0.1%
40 5
 
0.1%
39 20
 
0.5%
38 702
15.9%
37 1209
27.3%
36 8
 
0.2%
35 2
 
< 0.1%

Mothers_occupation
Real number (ℝ)

Zeros 

Distinct32
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.960895
Minimum0
Maximum194
Zeros144
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:07.825200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median5
Q39
95-th percentile9
Maximum194
Range194
Interquartile range (IQR)5

Descriptive statistics

Standard deviation26.418253
Coefficient of variation (CV)2.4102277
Kurtosis29.226145
Mean10.960895
Median Absolute Deviation (MAD)3
Skewness5.3392271
Sum48491
Variance697.92409
MonotonicityNot monotonic
2025-01-05T21:52:08.084971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 1577
35.6%
4 817
18.5%
5 530
 
12.0%
3 351
 
7.9%
2 318
 
7.2%
7 272
 
6.1%
0 144
 
3.3%
1 102
 
2.3%
6 91
 
2.1%
90 70
 
1.6%
Other values (22) 152
 
3.4%
ValueCountFrequency (%)
0 144
 
3.3%
1 102
 
2.3%
2 318
 
7.2%
3 351
 
7.9%
4 817
18.5%
5 530
 
12.0%
6 91
 
2.1%
7 272
 
6.1%
8 36
 
0.8%
9 1577
35.6%
ValueCountFrequency (%)
194 11
0.2%
193 4
 
0.1%
192 5
 
0.1%
191 26
0.6%
175 5
 
0.1%
173 1
 
< 0.1%
171 1
 
< 0.1%
153 2
 
< 0.1%
152 2
 
< 0.1%
151 3
 
0.1%

Fathers_occupation
Real number (ℝ)

Zeros 

Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.032324
Minimum0
Maximum195
Zeros128
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:08.358028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q39
95-th percentile10
Maximum195
Range195
Interquartile range (IQR)5

Descriptive statistics

Standard deviation25.26304
Coefficient of variation (CV)2.2899111
Kurtosis29.927395
Mean11.032324
Median Absolute Deviation (MAD)2
Skewness5.3951732
Sum48807
Variance638.2212
MonotonicityNot monotonic
2025-01-05T21:52:08.730816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
9 1010
22.8%
7 666
15.1%
5 516
11.7%
4 386
 
8.7%
3 384
 
8.7%
8 318
 
7.2%
10 266
 
6.0%
6 242
 
5.5%
2 197
 
4.5%
1 134
 
3.0%
Other values (36) 305
 
6.9%
ValueCountFrequency (%)
0 128
 
2.9%
1 134
 
3.0%
2 197
 
4.5%
3 384
 
8.7%
4 386
 
8.7%
5 516
11.7%
6 242
 
5.5%
7 666
15.1%
8 318
 
7.2%
9 1010
22.8%
ValueCountFrequency (%)
195 1
 
< 0.1%
194 2
 
< 0.1%
193 15
0.3%
192 6
 
0.1%
183 3
 
0.1%
182 2
 
< 0.1%
181 3
 
0.1%
175 4
 
0.1%
174 1
 
< 0.1%
172 2
 
< 0.1%

Admission_grade
Real number (ℝ)

High correlation 

Distinct620
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97812
Minimum95
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:09.050668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile103.415
Q1117.9
median126.1
Q3134.8
95-th percentile153.5
Maximum190
Range95
Interquartile range (IQR)16.9

Descriptive statistics

Standard deviation14.482001
Coefficient of variation (CV)0.11405115
Kurtosis0.6627246
Mean126.97812
Median Absolute Deviation (MAD)8.4
Skewness0.53059986
Sum561751.2
Variance209.72835
MonotonicityNot monotonic
2025-01-05T21:52:09.320554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 162
 
3.7%
140 153
 
3.5%
120 145
 
3.3%
100 116
 
2.6%
150 81
 
1.8%
110 77
 
1.7%
160 43
 
1.0%
128.2 39
 
0.9%
123 26
 
0.6%
128 26
 
0.6%
Other values (610) 3556
80.4%
ValueCountFrequency (%)
95 11
0.2%
95.1 1
 
< 0.1%
95.5 2
 
< 0.1%
95.8 1
 
< 0.1%
96 7
0.2%
96.1 1
 
< 0.1%
96.7 1
 
< 0.1%
97 6
0.1%
97.2 1
 
< 0.1%
97.4 1
 
< 0.1%
ValueCountFrequency (%)
190 3
0.1%
184.4 1
 
< 0.1%
184 1
 
< 0.1%
183.5 1
 
< 0.1%
180.4 1
 
< 0.1%
180 4
0.1%
179.6 1
 
< 0.1%
178.3 1
 
< 0.1%
178 1
 
< 0.1%
176.7 1
 
< 0.1%

Displaced
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.1 KiB
1
2426 
0
1998 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Length

2025-01-05T21:52:09.547696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T21:52:09.737916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring characters

ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2426
54.8%
0 1998
45.2%

Educational_special_needs
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.1 KiB
0
4373 
1
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Length

2025-01-05T21:52:09.953174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T21:52:10.140207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4373
98.8%
1 51
 
1.2%

Debtor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.1 KiB
0
3921 
1
503 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Length

2025-01-05T21:52:10.330879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T21:52:10.508093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3921
88.6%
1 503
 
11.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.1 KiB
1
3896 
0
528 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Length

2025-01-05T21:52:10.733182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T21:52:10.949076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring characters

ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3896
88.1%
0 528
 
11.9%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.1 KiB
0
2868 
1
1556 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Length

2025-01-05T21:52:11.150919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T21:52:11.341023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring characters

ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2868
64.8%
1 1556
35.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.1 KiB
0
3325 
1
1099 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Length

2025-01-05T21:52:11.532488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T21:52:11.718083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring characters

ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3325
75.2%
1 1099
 
24.8%

Age_at_enrollment
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.265145
Minimum17
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:11.961535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q119
median20
Q325
95-th percentile41
Maximum70
Range53
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.5878156
Coefficient of variation (CV)0.32614522
Kurtosis4.1268918
Mean23.265145
Median Absolute Deviation (MAD)2
Skewness2.0549884
Sum102925
Variance57.574946
MonotonicityNot monotonic
2025-01-05T21:52:12.232869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
18 1036
23.4%
19 911
20.6%
20 599
13.5%
21 322
 
7.3%
22 174
 
3.9%
24 131
 
3.0%
23 108
 
2.4%
26 94
 
2.1%
25 93
 
2.1%
27 91
 
2.1%
Other values (36) 865
19.6%
ValueCountFrequency (%)
17 5
 
0.1%
18 1036
23.4%
19 911
20.6%
20 599
13.5%
21 322
 
7.3%
22 174
 
3.9%
23 108
 
2.4%
24 131
 
3.0%
25 93
 
2.1%
26 94
 
2.1%
ValueCountFrequency (%)
70 1
 
< 0.1%
62 1
 
< 0.1%
61 1
 
< 0.1%
60 2
 
< 0.1%
59 3
0.1%
58 3
0.1%
57 2
 
< 0.1%
55 5
0.1%
54 7
0.2%
53 7
0.2%

International
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.1 KiB
0
4314 
1
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4424
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Length

2025-01-05T21:52:12.470493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T21:52:12.669228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4314
97.5%
1 110
 
2.5%

Curricular_units_1st_sem_credited
Real number (ℝ)

High correlation  Zeros 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70999096
Minimum0
Maximum20
Zeros3847
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:12.897987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3605066
Coefficient of variation (CV)3.3246995
Kurtosis19.205727
Mean0.70999096
Median Absolute Deviation (MAD)0
Skewness4.1690488
Sum3141
Variance5.5719915
MonotonicityNot monotonic
2025-01-05T21:52:13.174662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3847
87.0%
2 94
 
2.1%
1 85
 
1.9%
3 69
 
1.6%
6 51
 
1.2%
4 47
 
1.1%
7 41
 
0.9%
5 41
 
0.9%
8 31
 
0.7%
9 27
 
0.6%
Other values (11) 91
 
2.1%
ValueCountFrequency (%)
0 3847
87.0%
1 85
 
1.9%
2 94
 
2.1%
3 69
 
1.6%
4 47
 
1.1%
5 41
 
0.9%
6 51
 
1.2%
7 41
 
0.9%
8 31
 
0.7%
9 27
 
0.6%
ValueCountFrequency (%)
20 2
 
< 0.1%
19 2
 
< 0.1%
18 4
 
0.1%
17 3
 
0.1%
16 3
 
0.1%
15 5
 
0.1%
14 15
0.3%
13 13
0.3%
12 12
0.3%
11 17
0.4%

Curricular_units_1st_sem_enrolled
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2705696
Minimum0
Maximum26
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:13.422373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q15
median6
Q37
95-th percentile11
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4801782
Coefficient of variation (CV)0.39552677
Kurtosis8.9379154
Mean6.2705696
Median Absolute Deviation (MAD)1
Skewness1.6190409
Sum27741
Variance6.1512838
MonotonicityNot monotonic
2025-01-05T21:52:13.648120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 1910
43.2%
5 1010
22.8%
7 656
 
14.8%
8 296
 
6.7%
0 180
 
4.1%
12 66
 
1.5%
10 52
 
1.2%
11 45
 
1.0%
9 36
 
0.8%
15 25
 
0.6%
Other values (13) 148
 
3.3%
ValueCountFrequency (%)
0 180
 
4.1%
1 7
 
0.2%
2 9
 
0.2%
3 10
 
0.2%
4 21
 
0.5%
5 1010
22.8%
6 1910
43.2%
7 656
 
14.8%
8 296
 
6.7%
9 36
 
0.8%
ValueCountFrequency (%)
26 1
 
< 0.1%
23 2
 
< 0.1%
21 6
 
0.1%
19 2
 
< 0.1%
18 19
0.4%
17 16
0.4%
16 13
0.3%
15 25
0.6%
14 22
0.5%
13 20
0.5%

Curricular_units_1st_sem_evaluations
Real number (ℝ)

High correlation  Zeros 

Distinct35
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2990506
Minimum0
Maximum45
Zeros349
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:13.863918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum45
Range45
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1791056
Coefficient of variation (CV)0.50356429
Kurtosis5.4630252
Mean8.2990506
Median Absolute Deviation (MAD)2
Skewness0.9766367
Sum36715
Variance17.464923
MonotonicityNot monotonic
2025-01-05T21:52:14.109832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
8 791
17.9%
7 703
15.9%
6 598
13.5%
9 402
9.1%
0 349
7.9%
10 340
7.7%
11 239
 
5.4%
12 223
 
5.0%
5 220
 
5.0%
13 140
 
3.2%
Other values (25) 419
9.5%
ValueCountFrequency (%)
0 349
7.9%
1 6
 
0.1%
2 8
 
0.2%
3 6
 
0.1%
4 19
 
0.4%
5 220
 
5.0%
6 598
13.5%
7 703
15.9%
8 791
17.9%
9 402
9.1%
ValueCountFrequency (%)
45 2
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
29 2
< 0.1%
28 1
 
< 0.1%
27 2
< 0.1%
26 4
0.1%
25 3
0.1%

Curricular_units_1st_sem_approved
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7066004
Minimum0
Maximum26
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:14.335002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q36
95-th percentile9
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.094238
Coefficient of variation (CV)0.65742526
Kurtosis3.0966799
Mean4.7066004
Median Absolute Deviation (MAD)1
Skewness0.7662624
Sum20822
Variance9.5743087
MonotonicityNot monotonic
2025-01-05T21:52:14.548147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 1171
26.5%
5 723
16.3%
0 718
16.2%
7 471
10.6%
4 433
 
9.8%
3 269
 
6.1%
2 160
 
3.6%
1 127
 
2.9%
8 108
 
2.4%
11 49
 
1.1%
Other values (13) 195
 
4.4%
ValueCountFrequency (%)
0 718
16.2%
1 127
 
2.9%
2 160
 
3.6%
3 269
 
6.1%
4 433
 
9.8%
5 723
16.3%
6 1171
26.5%
7 471
10.6%
8 108
 
2.4%
9 40
 
0.9%
ValueCountFrequency (%)
26 1
 
< 0.1%
21 4
 
0.1%
20 3
 
0.1%
19 2
 
< 0.1%
18 15
0.3%
17 10
 
0.2%
16 5
 
0.1%
15 7
 
0.2%
14 14
0.3%
13 26
0.6%

Curricular_units_1st_sem_grade
Real number (ℝ)

High correlation  Zeros 

Distinct805
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.640822
Minimum0
Maximum18.875
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:14.791264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median12.285714
Q313.4
95-th percentile14.857143
Maximum18.875
Range18.875
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation4.8436634
Coefficient of variation (CV)0.45519637
Kurtosis0.90846103
Mean10.640822
Median Absolute Deviation (MAD)1.1571429
Skewness-1.5681456
Sum47074.995
Variance23.461075
MonotonicityNot monotonic
2025-01-05T21:52:15.089302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 718
 
16.2%
12 205
 
4.6%
13 147
 
3.3%
11 138
 
3.1%
11.5 89
 
2.0%
14 85
 
1.9%
12.5 84
 
1.9%
10 82
 
1.9%
12.66666667 82
 
1.9%
12.33333333 82
 
1.9%
Other values (795) 2712
61.3%
ValueCountFrequency (%)
0 718
16.2%
9.8 1
 
< 0.1%
10 82
 
1.9%
10.16666667 1
 
< 0.1%
10.2 8
 
0.2%
10.21428571 1
 
< 0.1%
10.25 7
 
0.2%
10.28571429 1
 
< 0.1%
10.33333333 16
 
0.4%
10.36842105 1
 
< 0.1%
ValueCountFrequency (%)
18.875 1
 
< 0.1%
18 2
 
< 0.1%
17.33333333 2
 
< 0.1%
17.125 1
 
< 0.1%
17.11111111 1
 
< 0.1%
17.00555556 1
 
< 0.1%
17 5
0.1%
16.9 1
 
< 0.1%
16.88571429 1
 
< 0.1%
16.85714286 1
 
< 0.1%
Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13765823
Minimum0
Maximum12
Zeros4130
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:15.366922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69088018
Coefficient of variation (CV)5.0188078
Kurtosis89.863208
Mean0.13765823
Median Absolute Deviation (MAD)0
Skewness8.2074031
Sum609
Variance0.47731543
MonotonicityNot monotonic
2025-01-05T21:52:15.582754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 4130
93.4%
1 153
 
3.5%
2 79
 
1.8%
3 23
 
0.5%
4 15
 
0.3%
6 6
 
0.1%
7 6
 
0.1%
5 5
 
0.1%
8 4
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
0 4130
93.4%
1 153
 
3.5%
2 79
 
1.8%
3 23
 
0.5%
4 15
 
0.3%
5 5
 
0.1%
6 6
 
0.1%
7 6
 
0.1%
8 4
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
10 1
 
< 0.1%
8 4
 
0.1%
7 6
 
0.1%
6 6
 
0.1%
5 5
 
0.1%
4 15
 
0.3%
3 23
 
0.5%
2 79
1.8%
1 153
3.5%

Curricular_units_2nd_sem_credited
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54181736
Minimum0
Maximum19
Zeros3894
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:15.783937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9185461
Coefficient of variation (CV)3.5409462
Kurtosis24.427266
Mean0.54181736
Median Absolute Deviation (MAD)0
Skewness4.6348195
Sum2397
Variance3.6808193
MonotonicityNot monotonic
2025-01-05T21:52:15.991741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 3894
88.0%
1 107
 
2.4%
2 92
 
2.1%
4 78
 
1.8%
5 68
 
1.5%
3 49
 
1.1%
6 26
 
0.6%
11 20
 
0.5%
7 16
 
0.4%
9 15
 
0.3%
Other values (9) 59
 
1.3%
ValueCountFrequency (%)
0 3894
88.0%
1 107
 
2.4%
2 92
 
2.1%
3 49
 
1.1%
4 78
 
1.8%
5 68
 
1.5%
6 26
 
0.6%
7 16
 
0.4%
8 12
 
0.3%
9 15
 
0.3%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 2
 
< 0.1%
16 2
 
< 0.1%
15 2
 
< 0.1%
14 4
 
0.1%
13 9
0.2%
12 14
0.3%
11 20
0.5%
10 13
0.3%
9 15
0.3%

Curricular_units_2nd_sem_enrolled
Real number (ℝ)

High correlation  Zeros 

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2321429
Minimum0
Maximum23
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:16.218107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median6
Q37
95-th percentile10
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1959508
Coefficient of variation (CV)0.35235886
Kurtosis7.13474
Mean6.2321429
Median Absolute Deviation (MAD)1
Skewness0.7881135
Sum27571
Variance4.8221997
MonotonicityNot monotonic
2025-01-05T21:52:16.444622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6 1913
43.2%
5 1054
23.8%
8 661
 
14.9%
7 304
 
6.9%
0 180
 
4.1%
11 60
 
1.4%
9 50
 
1.1%
10 48
 
1.1%
12 44
 
1.0%
13 37
 
0.8%
Other values (12) 73
 
1.7%
ValueCountFrequency (%)
0 180
 
4.1%
1 3
 
0.1%
2 5
 
0.1%
3 3
 
0.1%
4 17
 
0.4%
5 1054
23.8%
6 1913
43.2%
7 304
 
6.9%
8 661
 
14.9%
9 50
 
1.1%
ValueCountFrequency (%)
23 2
 
< 0.1%
21 1
 
< 0.1%
19 3
 
0.1%
18 2
 
< 0.1%
17 12
 
0.3%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 22
0.5%
13 37
0.8%
12 44
1.0%

Curricular_units_2nd_sem_evaluations
Real number (ℝ)

High correlation  Zeros 

Distinct30
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0632911
Minimum0
Maximum33
Zeros401
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:16.656427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum33
Range33
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.9479509
Coefficient of variation (CV)0.48962029
Kurtosis2.0682859
Mean8.0632911
Median Absolute Deviation (MAD)2
Skewness0.33649718
Sum35672
Variance15.586317
MonotonicityNot monotonic
2025-01-05T21:52:16.875274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
8 792
17.9%
6 614
13.9%
7 563
12.7%
9 456
10.3%
0 401
9.1%
10 355
8.0%
5 288
 
6.5%
11 255
 
5.8%
12 226
 
5.1%
13 126
 
2.8%
Other values (20) 348
7.9%
ValueCountFrequency (%)
0 401
9.1%
1 3
 
0.1%
2 4
 
0.1%
3 2
 
< 0.1%
4 10
 
0.2%
5 288
 
6.5%
6 614
13.9%
7 563
12.7%
8 792
17.9%
9 456
10.3%
ValueCountFrequency (%)
33 1
 
< 0.1%
28 1
 
< 0.1%
27 2
 
< 0.1%
26 3
 
0.1%
25 1
 
< 0.1%
24 3
 
0.1%
23 4
 
0.1%
22 10
0.2%
21 10
0.2%
20 8
0.2%

Curricular_units_2nd_sem_approved
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4358047
Minimum0
Maximum20
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:17.145202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0147639
Coefficient of variation (CV)0.67964306
Kurtosis0.84504466
Mean4.4358047
Median Absolute Deviation (MAD)2
Skewness0.30627938
Sum19624
Variance9.0888014
MonotonicityNot monotonic
2025-01-05T21:52:17.433730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6 965
21.8%
0 870
19.7%
5 726
16.4%
4 414
9.4%
7 331
 
7.5%
8 321
 
7.3%
3 285
 
6.4%
2 198
 
4.5%
1 114
 
2.6%
11 48
 
1.1%
Other values (10) 152
 
3.4%
ValueCountFrequency (%)
0 870
19.7%
1 114
 
2.6%
2 198
 
4.5%
3 285
 
6.4%
4 414
9.4%
5 726
16.4%
6 965
21.8%
7 331
 
7.5%
8 321
 
7.3%
9 36
 
0.8%
ValueCountFrequency (%)
20 2
 
< 0.1%
19 3
 
0.1%
18 2
 
< 0.1%
17 8
 
0.2%
16 2
 
< 0.1%
14 6
 
0.1%
13 21
0.5%
12 34
0.8%
11 48
1.1%
10 38
0.9%

Curricular_units_2nd_sem_grade
Real number (ℝ)

High correlation  Zeros 

Distinct786
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.230206
Minimum0
Maximum18.571429
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:17.726134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.75
median12.2
Q313.333333
95-th percentile14.980262
Maximum18.571429
Range18.571429
Interquartile range (IQR)2.5833333

Descriptive statistics

Standard deviation5.210808
Coefficient of variation (CV)0.50935515
Kurtosis0.066567351
Mean10.230206
Median Absolute Deviation (MAD)1.2
Skewness-1.3136502
Sum45258.43
Variance27.15252
MonotonicityNot monotonic
2025-01-05T21:52:18.008003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 870
 
19.7%
12 170
 
3.8%
11 165
 
3.7%
13 150
 
3.4%
11.5 86
 
1.9%
12.5 84
 
1.9%
14 77
 
1.7%
10 77
 
1.7%
13.5 65
 
1.5%
12.66666667 61
 
1.4%
Other values (776) 2619
59.2%
ValueCountFrequency (%)
0 870
19.7%
10 77
 
1.7%
10.16666667 4
 
0.1%
10.2 4
 
0.1%
10.25 10
 
0.2%
10.33333333 19
 
0.4%
10.375 1
 
< 0.1%
10.4 8
 
0.2%
10.42857143 2
 
< 0.1%
10.44444444 2
 
< 0.1%
ValueCountFrequency (%)
18.57142857 1
< 0.1%
17.71428571 1
< 0.1%
17.69230769 1
< 0.1%
17.6 2
< 0.1%
17.5875 1
< 0.1%
17.42857143 1
< 0.1%
17.16666667 1
< 0.1%
17 2
< 0.1%
16.90909091 1
< 0.1%
16.8 2
< 0.1%
Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15031646
Minimum0
Maximum12
Zeros4142
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:18.241328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75377407
Coefficient of variation (CV)5.0145812
Kurtosis66.811692
Mean0.15031646
Median Absolute Deviation (MAD)0
Skewness7.2677009
Sum665
Variance0.56817535
MonotonicityNot monotonic
2025-01-05T21:52:18.484389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 4142
93.6%
1 140
 
3.2%
2 48
 
1.1%
3 35
 
0.8%
4 21
 
0.5%
5 17
 
0.4%
6 8
 
0.2%
8 6
 
0.1%
7 5
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
0 4142
93.6%
1 140
 
3.2%
2 48
 
1.1%
3 35
 
0.8%
4 21
 
0.5%
5 17
 
0.4%
6 8
 
0.2%
7 5
 
0.1%
8 6
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
8 6
 
0.1%
7 5
 
0.1%
6 8
 
0.2%
5 17
 
0.4%
4 21
 
0.5%
3 35
 
0.8%
2 48
 
1.1%
1 140
 
3.2%
0 4142
93.6%

Unemployment_rate
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.566139
Minimum7.6
Maximum16.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2025-01-05T21:52:18.701401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile7.6
Q19.4
median11.1
Q313.9
95-th percentile16.2
Maximum16.2
Range8.6
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation2.6638505
Coefficient of variation (CV)0.23031458
Kurtosis-0.99552591
Mean11.566139
Median Absolute Deviation (MAD)1.7
Skewness0.21205105
Sum51168.6
Variance7.0960994
MonotonicityNot monotonic
2025-01-05T21:52:18.897805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7.6 571
12.9%
9.4 533
12.0%
10.8 525
11.9%
12.4 445
10.1%
12.7 419
9.5%
11.1 414
9.4%
15.5 397
9.0%
13.9 390
8.8%
8.9 368
8.3%
16.2 362
8.2%
ValueCountFrequency (%)
7.6 571
12.9%
8.9 368
8.3%
9.4 533
12.0%
10.8 525
11.9%
11.1 414
9.4%
12.4 445
10.1%
12.7 419
9.5%
13.9 390
8.8%
15.5 397
9.0%
16.2 362
8.2%
ValueCountFrequency (%)
16.2 362
8.2%
15.5 397
9.0%
13.9 390
8.8%
12.7 419
9.5%
12.4 445
10.1%
11.1 414
9.4%
10.8 525
11.9%
9.4 533
12.0%
8.9 368
8.3%
7.6 571
12.9%

Inflation_rate
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2280289
Minimum-0.8
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative923
Negative (%)20.9%
Memory size34.7 KiB
2025-01-05T21:52:19.087703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile-0.8
Q10.3
median1.4
Q32.6
95-th percentile3.7
Maximum3.7
Range4.5
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.3827107
Coefficient of variation (CV)1.1259594
Kurtosis-1.0390334
Mean1.2280289
Median Absolute Deviation (MAD)1.2
Skewness0.25237535
Sum5432.8
Variance1.9118889
MonotonicityNot monotonic
2025-01-05T21:52:19.286738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1.4 893
20.2%
2.6 571
12.9%
-0.8 533
12.0%
0.5 445
10.1%
3.7 419
9.5%
0.6 414
9.4%
2.8 397
9.0%
-0.3 390
8.8%
0.3 362
8.2%
ValueCountFrequency (%)
-0.8 533
12.0%
-0.3 390
8.8%
0.3 362
8.2%
0.5 445
10.1%
0.6 414
9.4%
1.4 893
20.2%
2.6 571
12.9%
2.8 397
9.0%
3.7 419
9.5%
ValueCountFrequency (%)
3.7 419
9.5%
2.8 397
9.0%
2.6 571
12.9%
1.4 893
20.2%
0.6 414
9.4%
0.5 445
10.1%
0.3 362
8.2%
-0.3 390
8.8%
-0.8 533
12.0%

GDP
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0019688065
Minimum-4.06
Maximum3.51
Zeros0
Zeros (%)0.0%
Negative1711
Negative (%)38.7%
Memory size34.7 KiB
2025-01-05T21:52:19.534605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-4.06
5-th percentile-4.06
Q1-1.7
median0.32
Q31.79
95-th percentile3.51
Maximum3.51
Range7.57
Interquartile range (IQR)3.49

Descriptive statistics

Standard deviation2.2699354
Coefficient of variation (CV)1152.95
Kurtosis-1.0016532
Mean0.0019688065
Median Absolute Deviation (MAD)1.47
Skewness-0.39406821
Sum8.71
Variance5.1526069
MonotonicityNot monotonic
2025-01-05T21:52:19.765148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.32 571
12.9%
-3.12 533
12.0%
1.74 525
11.9%
1.79 445
10.1%
-1.7 419
9.5%
2.02 414
9.4%
-4.06 397
9.0%
0.79 390
8.8%
3.51 368
8.3%
-0.92 362
8.2%
ValueCountFrequency (%)
-4.06 397
9.0%
-3.12 533
12.0%
-1.7 419
9.5%
-0.92 362
8.2%
0.32 571
12.9%
0.79 390
8.8%
1.74 525
11.9%
1.79 445
10.1%
2.02 414
9.4%
3.51 368
8.3%
ValueCountFrequency (%)
3.51 368
8.3%
2.02 414
9.4%
1.79 445
10.1%
1.74 525
11.9%
0.79 390
8.8%
0.32 571
12.9%
-0.92 362
8.2%
-1.7 419
9.5%
-3.12 533
12.0%
-4.06 397
9.0%

Status
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size245.0 KiB
Graduate
2209 
Dropout
1421 
Enrolled
794 

Length

Max length8
Median length8
Mean length7.6787975
Min length7

Characters and Unicode

Total characters33971
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDropout
2nd rowGraduate
3rd rowDropout
4th rowGraduate
5th rowGraduate

Common Values

ValueCountFrequency (%)
Graduate 2209
49.9%
Dropout 1421
32.1%
Enrolled 794
 
17.9%

Length

2025-01-05T21:52:20.019427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T21:52:20.235208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
graduate 2209
49.9%
dropout 1421
32.1%
enrolled 794
 
17.9%

Most occurring characters

ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Interactions

2025-01-05T21:51:49.734601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:48:50.738147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:48:57.022821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:02.243154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:08.954071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:14.476948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:21.697000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:30.251145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:38.822075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:47.968828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:54.127776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:59.878860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:06.091876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:12.094496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:17.529303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:23.517114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:29.910713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:36.202515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:42.094189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:48.048971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:56.557749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:03.519472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:09.576170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:15.436522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:22.150168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:28.189707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:35.360006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:41.992206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:50.189566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:48:50.997116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:48:57.182224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:02.395545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:09.192445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:14.652966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:21.996047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:30.828272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:39.074235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:48.157720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:54.327174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:00.068199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:06.269506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:12.279289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:17.721245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:23.699053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:30.573467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:36.443137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:42.312862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:48.240919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:56.841991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:03.722055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:09.785373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:15.658888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:22.356498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:28.420239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:35.566672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:42.184616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:50.631218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:48:51.199720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:48:57.329667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:02.545372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:09.404271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:14.816012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:22.317955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:31.228937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:39.346651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:48.340311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:54.532565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:00.267951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:06.437004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:12.443186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:17.906440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:23.900991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:30.793906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:36.703409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:42.572164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:48.423564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:57.161656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:03.891248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:09.986882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:15.886430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:22.541570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:28.652282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:35.757789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:42.540225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:50.970912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:48:51.461935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:48:57.536774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:02.686987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:09.588181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:15.009084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:22.564296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:31.560965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:39.629362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:48.504491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:54.699052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:00.471162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:06.601076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:12.621421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:18.089146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:24.079197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:30.990024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:36.921163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:42.804585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:48.593723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:57.404627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:04.065268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:10.204042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:16.091243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:22.721472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:28.877990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:35.936397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:42.849979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:51.277626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:48:51.637889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:48:57.748474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:02.841357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:09.761187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:15.206120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:22.820959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:31.886455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:39.957966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:48.685116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:54.888831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:00.790202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:06.786069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:12.804827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:18.355561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:24.259085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:31.191814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:37.126957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:43.011930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:48.776428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:57.632838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:04.259476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:10.401690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:16.309905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:22.932742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:29.186348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:36.857675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:43.173891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-01-05T21:50:05.686811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:11.638461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:17.190246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:23.107324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:29.405040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:35.701671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:41.706628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:47.648093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:55.969943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:03.113479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:09.178162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:15.011383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:21.677691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:27.793857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:34.894295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:41.573140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:48.725408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:59.557777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:48:56.807792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:02.003855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:08.635505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:14.298257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:21.376440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:29.864876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:38.528079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:47.698264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:53.920472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:49:59.692157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:05.883074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:11.858827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:17.348934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:23.301102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:29.685657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:35.931886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:41.896976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:47.843380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:50:56.261099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:03.304843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:09.355635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:15.198602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:21.907469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:27.968957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:35.116035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:41.743690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-05T21:51:49.120193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-05T21:52:20.494246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Admission_gradeAge_at_enrollmentApplication_modeApplication_orderCourseCurricular_units_1st_sem_approvedCurricular_units_1st_sem_creditedCurricular_units_1st_sem_enrolledCurricular_units_1st_sem_evaluationsCurricular_units_1st_sem_gradeCurricular_units_1st_sem_without_evaluationsCurricular_units_2nd_sem_approvedCurricular_units_2nd_sem_creditedCurricular_units_2nd_sem_enrolledCurricular_units_2nd_sem_evaluationsCurricular_units_2nd_sem_gradeCurricular_units_2nd_sem_without_evaluationsDaytime_evening_attendanceDebtorDisplacedEducational_special_needsFathers_occupationFathers_qualificationGDPGenderInflation_rateInternationalMarital_statusMothers_occupationMothers_qualificationNacionalityPrevious_qualificationPrevious_qualification_gradeScholarship_holderStatusTuition_fees_up_to_dateUnemployment_rate
Admission_grade1.000-0.102-0.024-0.114-0.0770.1000.075-0.006-0.0950.209-0.0000.0990.0750.001-0.0610.189-0.0240.1040.0680.1360.000-0.039-0.023-0.0400.032-0.0180.1410.001-0.024-0.0330.0170.1380.5850.0900.1060.0680.027
Age_at_enrollment-0.1021.0000.543-0.366-0.073-0.1660.2990.0010.171-0.2100.061-0.1880.301-0.0310.083-0.2130.0940.4890.1330.3890.0000.0380.086-0.0570.1620.0180.0000.4830.1060.1720.0200.389-0.1450.2120.2190.2090.019
Application_mode-0.0240.5431.000-0.285-0.087-0.0800.3510.0660.199-0.1550.029-0.1040.3440.0460.150-0.1400.0510.3950.1450.3970.014-0.0170.046-0.0210.174-0.0260.0670.2700.0310.0690.0030.424-0.0290.2130.2160.1950.091
Application_order-0.114-0.366-0.2851.0000.1330.090-0.2020.056-0.0960.074-0.0410.106-0.2010.075-0.0530.065-0.0300.1850.0900.3820.000-0.023-0.0490.0300.108-0.0110.000-0.174-0.044-0.058-0.020-0.253-0.0710.0890.0790.073-0.104
Course-0.077-0.073-0.0870.1331.0000.143-0.1690.1790.0010.119-0.1090.157-0.1650.1880.0180.146-0.0320.6470.0170.1860.0250.0230.036-0.0250.1190.0260.0240.0320.0370.053-0.004-0.145-0.0560.0180.0390.000-0.036
Curricular_units_1st_sem_approved0.100-0.166-0.0800.0900.1431.0000.3620.7070.2620.640-0.0690.8920.3670.6990.3640.663-0.0730.1730.1450.1290.0000.0140.0020.0590.237-0.0020.000-0.0610.000-0.0180.001-0.0390.0930.2640.4550.2800.074
Curricular_units_1st_sem_credited0.0750.2990.351-0.202-0.1690.3621.0000.4230.3660.1010.1000.2890.9140.3760.3310.0930.0640.1710.0570.1070.000-0.027-0.0070.0220.027-0.0020.0000.102-0.017-0.0010.0230.3190.0100.0860.0450.0000.024
Curricular_units_1st_sem_enrolled-0.0060.0010.0660.0560.1790.7070.4231.0000.4200.363-0.0190.6530.4400.9620.4310.351-0.0210.2190.0470.1500.0430.004-0.0000.0180.2130.0170.000-0.0010.0040.0170.0050.0720.0350.1560.1730.0830.108
Curricular_units_1st_sem_evaluations-0.0950.1710.199-0.0960.0010.2620.3660.4201.0000.1120.2020.2520.3690.3860.6940.0900.1530.0630.0610.0980.000-0.0330.035-0.0970.079-0.0400.0560.059-0.0060.0380.0060.115-0.0690.1870.2580.1030.067
Curricular_units_1st_sem_grade0.209-0.210-0.1550.0740.1190.6400.1010.3630.1121.000-0.0170.6290.0970.3680.1820.762-0.0470.1380.1060.0870.000-0.005-0.0230.0920.193-0.0370.000-0.085-0.026-0.0510.011-0.0330.1820.1810.3830.2480.045
Curricular_units_1st_sem_without_evaluations-0.0000.0610.029-0.041-0.109-0.0690.100-0.0190.202-0.0171.000-0.0550.059-0.0320.096-0.0410.3840.0190.0310.0000.000-0.042-0.003-0.1840.000-0.0680.0570.043-0.014-0.0060.0100.0220.0030.0570.0620.078-0.067
Curricular_units_2nd_sem_approved0.099-0.188-0.1040.1060.1570.8920.2890.6530.2520.629-0.0551.0000.3190.6740.3030.694-0.0640.1010.1800.1160.0000.0290.0150.0480.262-0.0150.025-0.0590.022-0.009-0.016-0.0520.0820.2730.5160.3130.070
Curricular_units_2nd_sem_credited0.0750.3010.344-0.201-0.1650.3670.9140.4400.3690.0970.0590.3191.0000.4140.3420.1030.0820.1820.0540.1280.000-0.0150.0030.0240.028-0.0010.0000.102-0.0150.0060.0190.314-0.0010.0750.0420.0000.012
Curricular_units_2nd_sem_enrolled0.001-0.0310.0460.0750.1880.6990.3760.9620.3860.368-0.0320.6740.4141.0000.4400.364-0.0270.1870.0750.1380.0000.005-0.0030.0190.1620.0090.030-0.0200.0020.004-0.0080.0550.0360.1130.1390.1240.139
Curricular_units_2nd_sem_evaluations-0.0610.0830.150-0.0530.0180.3640.3310.4310.6940.1820.0960.3030.3420.4401.0000.1720.1590.1080.0590.0680.029-0.0230.006-0.0040.109-0.0240.0110.009-0.0130.013-0.0160.072-0.0610.1660.2740.1240.061
Curricular_units_2nd_sem_grade0.189-0.213-0.1400.0650.1460.6630.0930.3510.0900.762-0.0410.6940.1030.3640.1721.000-0.0520.0850.1500.0690.0000.012-0.0170.1050.201-0.0430.000-0.086-0.002-0.041-0.003-0.0440.1530.1960.4550.2970.042
Curricular_units_2nd_sem_without_evaluations-0.0240.0940.051-0.030-0.032-0.0730.064-0.0210.153-0.0470.384-0.0640.082-0.0270.159-0.0521.0000.0000.0630.0360.000-0.054-0.001-0.1110.055-0.0270.0000.0560.0080.032-0.0180.033-0.0160.0400.0660.071-0.053
Daytime_evening_attendance0.1040.4890.3950.1850.6470.1730.1710.2190.0630.1380.0190.1010.1820.1870.1080.0850.0001.0000.0000.2510.0230.0550.1940.0930.0000.0730.0210.3660.0580.2300.0000.1480.1250.0920.0780.0350.093
Debtor0.0680.1330.1450.0900.0170.1450.0570.0470.0610.1060.0310.1800.0540.0750.0590.1500.0630.0001.0000.0880.0000.1030.0000.1380.0510.0860.0720.0300.0980.0320.0800.1430.0630.0650.2410.4070.135
Displaced0.1360.3890.3970.3820.1860.1290.1070.1500.0980.0870.0000.1160.1280.1380.0680.0690.0360.2510.0881.0000.0000.0800.0730.1400.1240.0580.0000.2750.0650.1050.0000.1690.0940.0710.1120.0940.138
Educational_special_needs0.0000.0000.0140.0000.0250.0000.0000.0430.0000.0000.0000.0000.0000.0000.0290.0000.0000.0230.0000.0001.0000.0330.0000.0570.0030.0420.0000.0000.0120.0090.0000.0000.0000.0110.0000.0000.048
Fathers_occupation-0.0390.038-0.017-0.0230.0230.014-0.0270.004-0.033-0.005-0.0420.029-0.0150.005-0.0230.012-0.0540.0550.1030.0800.0331.0000.2440.0540.0000.0450.0560.0580.4790.1630.017-0.003-0.0330.0460.1080.0530.002
Fathers_qualification-0.0230.0860.046-0.0490.0360.002-0.007-0.0000.035-0.023-0.0030.0150.003-0.0030.006-0.017-0.0010.1940.0000.0730.0000.2441.000-0.0620.0830.0400.0880.1220.2520.434-0.0850.015-0.0090.1460.1080.041-0.048
GDP-0.040-0.057-0.0210.030-0.0250.0590.0220.018-0.0970.092-0.1840.0480.0240.019-0.0040.105-0.1110.0930.1380.1400.0570.054-0.0621.0000.081-0.1020.065-0.0680.029-0.0890.0540.028-0.0720.1290.0520.079-0.288
Gender0.0320.1620.1740.1080.1190.2370.0270.2130.0790.1930.0000.2620.0280.1620.1090.2010.0550.0000.0510.1240.0030.0000.0830.0811.0000.0680.0200.0440.0080.0740.0130.1080.0310.1680.2290.1020.076
Inflation_rate-0.0180.018-0.026-0.0110.026-0.002-0.0020.017-0.040-0.037-0.068-0.015-0.0010.009-0.024-0.043-0.0270.0730.0860.0580.0420.0450.040-0.1020.0681.0000.030-0.0000.0410.042-0.003-0.0420.0190.0980.0360.086-0.055
International0.1410.0000.0670.0000.0240.0000.0000.0000.0560.0000.0570.0250.0000.0300.0110.0000.0000.0210.0720.0000.0000.0560.0880.0650.0200.0301.0000.0000.0400.0740.9120.0000.0990.0220.0000.0390.069
Marital_status0.0010.4830.270-0.1740.032-0.0610.102-0.0010.059-0.0850.043-0.0590.102-0.0200.009-0.0860.0560.3660.0300.2750.0000.0580.122-0.0680.044-0.0000.0001.0000.1210.193-0.0300.189-0.0220.1040.0780.093-0.038
Mothers_occupation-0.0240.1060.031-0.0440.0370.000-0.0170.004-0.006-0.026-0.0140.022-0.0150.002-0.013-0.0020.0080.0580.0980.0650.0120.4790.2520.0290.0080.0410.0400.1211.0000.3520.032-0.010-0.0290.0530.1180.0930.002
Mothers_qualification-0.0330.1720.069-0.0580.053-0.018-0.0010.0170.038-0.051-0.006-0.0090.0060.0040.013-0.0410.0320.2300.0320.1050.0090.1630.434-0.0890.0740.0420.0740.1930.3521.000-0.0350.026-0.0380.1160.1250.054-0.087
Nacionality0.0170.0200.003-0.020-0.0040.0010.0230.0050.0060.0110.010-0.0160.019-0.008-0.016-0.003-0.0180.0000.0800.0000.0000.017-0.0850.0540.013-0.0030.912-0.0300.032-0.0351.000-0.0410.0420.0100.0160.047-0.011
Previous_qualification0.1380.3890.424-0.253-0.145-0.0390.3190.0720.115-0.0330.022-0.0520.3140.0550.072-0.0440.0330.1480.1430.1690.000-0.0030.0150.0280.108-0.0420.0000.189-0.0100.026-0.0411.0000.0680.0810.1110.1230.082
Previous_qualification_grade0.585-0.145-0.029-0.071-0.0560.0930.0100.035-0.0690.1820.0030.082-0.0010.036-0.0610.153-0.0160.1250.0630.0940.000-0.033-0.009-0.0720.0310.0190.099-0.022-0.029-0.0380.0420.0681.0000.0640.0970.0790.048
Scholarship_holder0.0900.2120.2130.0890.0180.2640.0860.1560.1870.1810.0570.2730.0750.1130.1660.1960.0400.0920.0650.0710.0110.0460.1460.1290.1680.0980.0220.1040.0530.1160.0100.0810.0641.0000.3040.1360.130
Status0.1060.2190.2160.0790.0390.4550.0450.1730.2580.3830.0620.5160.0420.1390.2740.4550.0660.0780.2410.1120.0000.1080.1080.0520.2290.0360.0000.0780.1180.1250.0160.1110.0970.3041.0000.4310.053
Tuition_fees_up_to_date0.0680.2090.1950.0730.0000.2800.0000.0830.1030.2480.0780.3130.0000.1240.1240.2970.0710.0350.4070.0940.0000.0530.0410.0790.1020.0860.0390.0930.0930.0540.0470.1230.0790.1360.4311.0000.091
Unemployment_rate0.0270.0190.091-0.104-0.0360.0740.0240.1080.0670.045-0.0670.0700.0120.1390.0610.042-0.0530.0930.1350.1380.0480.002-0.048-0.2880.076-0.0550.069-0.0380.002-0.087-0.0110.0820.0480.1300.0530.0911.000

Missing values

2025-01-05T21:52:00.476215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-05T21:52:01.586752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Marital_statusApplication_modeApplication_orderCourseDaytime_evening_attendancePrevious_qualificationPrevious_qualification_gradeNacionalityMothers_qualificationFathers_qualificationMothers_occupationFathers_occupationAdmission_gradeDisplacedEducational_special_needsDebtorTuition_fees_up_to_dateGenderScholarship_holderAge_at_enrollmentInternationalCurricular_units_1st_sem_creditedCurricular_units_1st_sem_enrolledCurricular_units_1st_sem_evaluationsCurricular_units_1st_sem_approvedCurricular_units_1st_sem_gradeCurricular_units_1st_sem_without_evaluationsCurricular_units_2nd_sem_creditedCurricular_units_2nd_sem_enrolledCurricular_units_2nd_sem_evaluationsCurricular_units_2nd_sem_approvedCurricular_units_2nd_sem_gradeCurricular_units_2nd_sem_without_evaluationsUnemployment_rateInflation_rateGDPStatus
0117517111122.01191259127.310011020000000.000000000000.000000010.81.41.74Dropout
11151925411160.011333142.5100010190066614.0000000066613.666667013.9-0.30.79Graduate
2115907011122.01373799124.810001019006000.000000006000.000000010.81.41.74Dropout
31172977311122.01383753119.6100100200068613.42857100610512.40000009.4-0.8-3.12Graduate
42391801401100.01373899141.5000100450069512.3333330066613.000000013.9-0.30.79Graduate
523919991019133.11373797114.80011105000510511.85714300517511.500000516.20.3-0.92Graduate
6111950011142.011938710128.4100101180079713.3000000088814.345000015.52.8-4.06Graduate
71184925411119.01373799113.110001022005500.000000005500.000000015.52.8-4.06Dropout
8113923811137.0621199129.3000101211068613.8750000067614.142857016.20.3-0.92Graduate
9111923811138.0111947123.0101000180069511.40000000614213.50000008.91.43.51Dropout
Marital_statusApplication_modeApplication_orderCourseDaytime_evening_attendancePrevious_qualificationPrevious_qualification_gradeNacionalityMothers_qualificationFathers_qualificationMothers_occupationFathers_occupationAdmission_gradeDisplacedEducational_special_needsDebtorTuition_fees_up_to_dateGenderScholarship_holderAge_at_enrollmentInternationalCurricular_units_1st_sem_creditedCurricular_units_1st_sem_enrolledCurricular_units_1st_sem_evaluationsCurricular_units_1st_sem_approvedCurricular_units_1st_sem_gradeCurricular_units_1st_sem_without_evaluationsCurricular_units_2nd_sem_creditedCurricular_units_2nd_sem_enrolledCurricular_units_2nd_sem_evaluationsCurricular_units_2nd_sem_approvedCurricular_units_2nd_sem_gradeCurricular_units_2nd_sem_without_evaluationsUnemployment_rateInflation_rateGDPStatus
4414111913011137.0133835129.3100100180056511.8000001058511.60000009.4-0.8-3.12Graduate
441543919500119133.11373766117.80010004600714312.33333300712311.083333011.10.62.02Dropout
44161432950011136.01383895131.30001002301114151212.62500011114151212.62500017.62.60.32Graduate
4417111907011132.011199133.8100101200066613.8333330066613.500000016.20.3-0.92Graduate
441814419070139120.0133839120.0000110200277612.50000005910713.142857116.20.3-0.92Graduate
4419116977311125.011154122.2000110190067513.6000000068512.666667015.52.8-4.06Graduate
4420112977311120.01051199119.0101000181066612.0000000066211.000000011.10.62.02Dropout
4421111950011154.01373799149.5100101300078714.9125000089113.500000013.9-0.30.79Dropout
4422111914711180.01373774153.8100101200055513.8000000056512.00000009.4-0.8-3.12Graduate
44231101977311152.022383759152.0100100221068611.6666670066613.000000012.73.7-1.70Graduate